SmartPredict
  • Documentation
  • OVERVIEW
    • Presentation
      • Key features
      • Who may benefit from its use
      • The SmartPredict Graphical User Interface (GUI)
        • The SmartPredict Modules
        • Focus on the Notebooks
        • Practical shortcuts
    • Prerequisites
  • Getting started
    • Getting started (Part1) : Iris classification project
      • Project description
      • Step 1. Create the project
      • Step 2. Upload the dataset
      • Step 3. Preprocess the dataset
      • Step 4. Build the flowchart
        • Set up the flowchart
        • Configure the modules
      • Step 5. Run the build
      • Step 6. Deploy the project
      • Step 7. Make inferences with our model
      • Conclusion
  • Getting started (Part 2): Predicting the passengers' survival in the Titanic shipwreck
    • Project description
    • Step 1. Create the project
    • Step 2. Upload the dataset
    • Step 3. Preprocess the dataset
    • Step 4. Build the flowchart
    • Step 5. Run the build
    • Step 6. Deploy the pipeline
    • Step 7. Make inferences with our pipeline
  • MODULE REFERENCE
    • CORE MODULES
      • Introduction
      • Basic Operations
        • Item Saver
      • Web Services
        • Web Service IN and OUT
      • Data retrieval
        • Data fetcher
        • Data frame loader/converter
        • Image data loader
      • Data preprocessing
        • Introduction
        • Array Reshaper
        • Generic Data Preprocessor
        • Missing data handler
        • Normalizer
        • One Hot Encoder
        • Ordinal Encoder
      • Data selection
        • Features selector
        • Generic data splitter
        • Labeled data splitter
      • Training and Prediction
        • Predictor DL models
        • Predictor ML models
        • Predictor ML models (Probabilistic models)
        • Trainer ML models
        • Trainer/Evaluator DL models
      • Evaluation and fine-tuning
        • Cross Validator for ML
        • Evaluator for ML models
      • Machine Learning algorithms
        • ML modules in SmartPredict
        • Decision Tree Regressor
        • KNeighbors Classifier
        • KNeighbors Regressors
        • Linear Regressor
        • Logistic Regressor
        • MLP Regressor
        • Naive Bayes Classifier
        • Random Forest Classifier
        • Random Forest Regressor
        • Support Vector Classifier
        • Support Vector Regressor
        • XGBoost Classifier
        • XGBoost Regressor
      • Deep learning algorithms
        • Dense Neural Network
        • Recurrent Neural Networks
      • Computer Vision
        • Convolutional Recurrent Networks
        • Fully Convolutional Neural Networks
        • Face detector
        • Image IO
        • Image matcher
        • Yolo
      • Natural Language Processing
        • Introduction
        • Text cleaner
        • Text vectorizer
      • Times Series processing
        • TS features selector
      • TensorFlow API
        • LSTM Layer
        • Dense Layer
      • Helpers
        • Data/Object Logger
        • Object Selector (5 ports)
      • Conclusion
  • CUSTOM MODULES
    • Function
    • Class
    • Use cases
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  1. MODULE REFERENCE
  2. CORE MODULES
  3. Machine Learning algorithms

Random Forest Regressor

This module belongs to the " Machine Learning algorithms" category.

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Last updated 5 years ago

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Description

This module is a parameterizable and trainable Random Forest regression model.

It contains functions to variate the sample leaf nodes, impurity, features and depth, just like the Random Forest Classifier.

Parameters

whereas features can be :

  • auto,

  • SQRT

  • Log2

  • Integer

  • Float

  • None

For forecasting accuracy , as criteria of measure, we have the choice between mse (stands for : mean squared error ) and mae (mean absolute error) . (for more details refer to )

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